Refactor tab2 layout with expanded display options and improved data management

app.py

@@ -416,250 +416,252 @@ with tab1:
                 )
 
 with tab2:
-    col1, col2 = st.columns([1, 7])
-    with col1:
-        if st.button("Load/Reset Data", key='reset2'):
-            st.cache_data.clear()
-            dk_raw, fd_raw, dk_raw_sec, fd_raw_sec, roo_raw, timestamp, roo_backlog = load_overall_stats()
-            dk_lineups = init_DK_lineups()
-            fd_lineups = init_FD_lineups()
-            t_stamp = f"Last Update: " + str(timestamp) + f" CST"
-            for key in st.session_state.keys():
-                del st.session_state[key]
-              
-        slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Just the Main Slate'))
-        site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'))
-        lineup_num_var = st.number_input("How many lineups do you want to display?", min_value=1, max_value=1000, value=150, step=1)
-
-        if site_var1 == 'Draftkings':
-            raw_baselines = dk_raw
-            ROO_slice = roo_raw[roo_raw['site'] == 'Draftkings']
-            id_dict = dict(zip(ROO_slice.Player, ROO_slice.player_ID))
-            # Get the minimum and maximum ownership values from dk_lineups
-            min_own = np.min(dk_lineups[:,14])
-            max_own = np.max(dk_lineups[:,14])
-            column_names = dk_columns
+    if st.button("Load/Reset Data", key='reset2'):
+        st.cache_data.clear()
+        dk_raw, fd_raw, dk_raw_sec, fd_raw_sec, roo_raw, timestamp, roo_backlog = load_overall_stats()
+        dk_lineups = init_DK_lineups()
+        fd_lineups = init_FD_lineups()
+        t_stamp = f"Last Update: " + str(timestamp) + f" CST"
+        for key in st.session_state.keys():
+            del st.session_state[key]
+
+    with st.expander("Display Options"):
+        col1, col2, col3, col4, col5 = st.columns(5)
+        with col1:
+            slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Just the Main Slate'))
+        with col2:
+            site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'))
+        with col3:
+            lineup_num_var = st.number_input("How many lineups do you want to display?", min_value=1, max_value=1000, value=150, step=1)
+        with col4:
+            if site_var1 == 'Draftkings':
+                raw_baselines = dk_raw
+                ROO_slice = roo_raw[roo_raw['site'] == 'Draftkings']
+                id_dict = dict(zip(ROO_slice.Player, ROO_slice.player_ID))
+                # Get the minimum and maximum ownership values from dk_lineups
+                min_own = np.min(dk_lineups[:,14])
+                max_own = np.max(dk_lineups[:,14])
+                column_names = dk_columns
+                
+                player_var1 = st.radio("Do you want a frame with specific Players?", ('Full Slate', 'Specific Players'), key='player_var1')
+                if player_var1 == 'Specific Players':
+                        player_var2 = st.multiselect('Which players do you want?', options = dk_raw['Player'].unique())
+                elif player_var1 == 'Full Slate':
+                        player_var2 = dk_raw.Player.values.tolist()
+                        
+            elif site_var1 == 'Fanduel':
+                raw_baselines = fd_raw
+                ROO_slice = roo_raw[roo_raw['site'] == 'Fanduel']
+                id_dict = dict(zip(ROO_slice.Player, ROO_slice.player_ID))
+                min_own = np.min(fd_lineups[:,15])
+                max_own = np.max(fd_lineups[:,15])
+                column_names = fd_columns
+                
+                player_var1 = st.radio("Do you want a frame with specific Players?", ('Full Slate', 'Specific Players'), key='player_var1')
+                if player_var1 == 'Specific Players':
+                        player_var2 = st.multiselect('Which players do you want?', options = fd_raw['Player'].unique())
+                elif player_var1 == 'Full Slate':
+                        player_var2 = fd_raw.Player.values.tolist()
+        with col5:
+            if st.button("Prepare data export", key='data_export'):
+                data_export = st.session_state.working_seed.copy()
+                if site_var1 == 'Draftkings':
+                    for col_idx in range(8):
+                        data_export[:, col_idx] = np.array([id_dict.get(player, player) for player in data_export[:, col_idx]])
+                elif site_var1 == 'Fanduel':
+                    for col_idx in range(9):
+                        data_export[:, col_idx] = np.array([id_dict.get(player, player) for player in data_export[:, col_idx]])
+                st.download_button(
+                    label="Export optimals set",
+                    data=convert_df(data_export),
+                    file_name='NBA_optimals_export.csv',
+                    mime='text/csv',
+            )
             
-            player_var1 = st.radio("Do you want a frame with specific Players?", ('Full Slate', 'Specific Players'), key='player_var1')
+    if site_var1 == 'Draftkings':
+        if 'working_seed' in st.session_state:
+            st.session_state.working_seed = st.session_state.working_seed
             if player_var1 == 'Specific Players':
-                    player_var2 = st.multiselect('Which players do you want?', options = dk_raw['Player'].unique())
+                st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)]
             elif player_var1 == 'Full Slate':
-                    player_var2 = dk_raw.Player.values.tolist()
-                    
-        elif site_var1 == 'Fanduel':
-            raw_baselines = fd_raw
-            ROO_slice = roo_raw[roo_raw['site'] == 'Fanduel']
-            id_dict = dict(zip(ROO_slice.Player, ROO_slice.player_ID))
-            min_own = np.min(fd_lineups[:,15])
-            max_own = np.max(fd_lineups[:,15])
-            column_names = fd_columns
-            
-            player_var1 = st.radio("Do you want a frame with specific Players?", ('Full Slate', 'Specific Players'), key='player_var1')
+                st.session_state.working_seed = dk_lineups.copy()
+            st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names)
+        elif 'working_seed' not in st.session_state:
+            st.session_state.working_seed = dk_lineups.copy()
+            st.session_state.working_seed = st.session_state.working_seed
             if player_var1 == 'Specific Players':
-                    player_var2 = st.multiselect('Which players do you want?', options = fd_raw['Player'].unique())
+                st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)]
             elif player_var1 == 'Full Slate':
-                    player_var2 = fd_raw.Player.values.tolist()
-
-        if st.button("Prepare data export", key='data_export'):
-            data_export = st.session_state.working_seed.copy()
-            if site_var1 == 'Draftkings':
-                for col_idx in range(8):
-                    data_export[:, col_idx] = np.array([id_dict.get(player, player) for player in data_export[:, col_idx]])
-            elif site_var1 == 'Fanduel':
-                for col_idx in range(9):
-                    data_export[:, col_idx] = np.array([id_dict.get(player, player) for player in data_export[:, col_idx]])
-            st.download_button(
-                label="Export optimals set",
-                data=convert_df(data_export),
-                file_name='NBA_optimals_export.csv',
-                mime='text/csv',
-            )
-    with col2:
-        
-        if site_var1 == 'Draftkings':
-            if 'working_seed' in st.session_state:
-                st.session_state.working_seed = st.session_state.working_seed
-                if player_var1 == 'Specific Players':
-                    st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)]
-                elif player_var1 == 'Full Slate':
-                    st.session_state.working_seed = dk_lineups.copy()
-                st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names)
-            elif 'working_seed' not in st.session_state:
                 st.session_state.working_seed = dk_lineups.copy()
-                st.session_state.working_seed = st.session_state.working_seed
-                if player_var1 == 'Specific Players':
-                    st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)]
-                elif player_var1 == 'Full Slate':
-                    st.session_state.working_seed = dk_lineups.copy()
-                st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names)
+            st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names)
+        
+    elif site_var1 == 'Fanduel':
+        if 'working_seed' in st.session_state:
+            st.session_state.working_seed = st.session_state.working_seed
+            if player_var1 == 'Specific Players':
+                st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)]
+            elif player_var1 == 'Full Slate':
+                st.session_state.working_seed = fd_lineups.copy()
+            st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names)
+        elif 'working_seed' not in st.session_state:
+            st.session_state.working_seed = fd_lineups.copy()
+            st.session_state.working_seed = st.session_state.working_seed
+            if player_var1 == 'Specific Players':
+                st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)]
+            elif player_var1 == 'Full Slate':
+                st.session_state.working_seed = fd_lineups.copy()
+            st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names)
+
+    export_file = st.session_state.data_export_display.copy()
+    if site_var1 == 'Draftkings':
+        for col_idx in range(8):
+            export_file.iloc[:, col_idx] = export_file.iloc[:, col_idx].map(id_dict)
+    elif site_var1 == 'Fanduel':
+        for col_idx in range(9):
+            export_file.iloc[:, col_idx] = export_file.iloc[:, col_idx].map(id_dict)
             
-        elif site_var1 == 'Fanduel':
-            if 'working_seed' in st.session_state:
-                st.session_state.working_seed = st.session_state.working_seed
-                if player_var1 == 'Specific Players':
-                    st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)]
-                elif player_var1 == 'Full Slate':
-                    st.session_state.working_seed = fd_lineups.copy()
-                st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names)
-            elif 'working_seed' not in st.session_state:
+    with st.container():
+        if st.button("Reset Optimals", key='reset3'):
+            for key in st.session_state.keys():
+                del st.session_state[key]
+            if site_var1 == 'Draftkings':
+                st.session_state.working_seed = dk_lineups.copy()
+            elif site_var1 == 'Fanduel':
                 st.session_state.working_seed = fd_lineups.copy()
-                st.session_state.working_seed = st.session_state.working_seed
-                if player_var1 == 'Specific Players':
-                    st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)]
-                elif player_var1 == 'Full Slate':
-                    st.session_state.working_seed = fd_lineups.copy()
-                st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names)
-
-        export_file = st.session_state.data_export_display.copy()
-        if site_var1 == 'Draftkings':
-            for col_idx in range(8):
-                export_file.iloc[:, col_idx] = export_file.iloc[:, col_idx].map(id_dict)
-        elif site_var1 == 'Fanduel':
-            for col_idx in range(9):
-                export_file.iloc[:, col_idx] = export_file.iloc[:, col_idx].map(id_dict)
-                
-        with st.container():
-            if st.button("Reset Optimals", key='reset3'):
-                for key in st.session_state.keys():
-                    del st.session_state[key]
+        if 'data_export_display' in st.session_state:
+            st.dataframe(st.session_state.data_export_display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), height=500, use_container_width = True)
+        st.download_button(
+            label="Export display optimals",
+            data=convert_df(export_file),
+            file_name='NBA_display_optimals.csv',
+            mime='text/csv',
+        )
+    
+    with st.container():
+        if 'working_seed' in st.session_state:
+            # Create a new dataframe with summary statistics
+            if site_var1 == 'Draftkings':
+                summary_df = pd.DataFrame({
+                    'Metric': ['Min', 'Average', 'Max', 'STDdev'],
+                    'Salary': [
+                        np.min(st.session_state.working_seed[:,8]),
+                        np.mean(st.session_state.working_seed[:,8]),
+                        np.max(st.session_state.working_seed[:,8]),
+                        np.std(st.session_state.working_seed[:,8])
+                    ],
+                    'Proj': [
+                        np.min(st.session_state.working_seed[:,9]),
+                        np.mean(st.session_state.working_seed[:,9]),
+                        np.max(st.session_state.working_seed[:,9]),
+                        np.std(st.session_state.working_seed[:,9])
+                    ],
+                    'Own': [
+                        np.min(st.session_state.working_seed[:,14]),
+                        np.mean(st.session_state.working_seed[:,14]),
+                        np.max(st.session_state.working_seed[:,14]),
+                        np.std(st.session_state.working_seed[:,14])
+                    ]
+                })
+            elif site_var1 == 'Fanduel':
+                summary_df = pd.DataFrame({
+                    'Metric': ['Min', 'Average', 'Max', 'STDdev'],
+                    'Salary': [
+                        np.min(st.session_state.working_seed[:,9]),
+                        np.mean(st.session_state.working_seed[:,9]),
+                        np.max(st.session_state.working_seed[:,9]),
+                        np.std(st.session_state.working_seed[:,9])
+                    ],
+                    'Proj': [
+                        np.min(st.session_state.working_seed[:,10]),
+                        np.mean(st.session_state.working_seed[:,10]),
+                        np.max(st.session_state.working_seed[:,10]),
+                        np.std(st.session_state.working_seed[:,10])
+                    ],
+                    'Own': [
+                        np.min(st.session_state.working_seed[:,15]),
+                        np.mean(st.session_state.working_seed[:,15]),
+                        np.max(st.session_state.working_seed[:,15]),
+                        np.std(st.session_state.working_seed[:,15])
+                    ]
+                })
+
+            # Set the index of the summary dataframe as the "Metric" column
+            summary_df = summary_df.set_index('Metric')
+
+            # Display the summary dataframe
+            st.subheader("Optimal Statistics")
+            st.dataframe(summary_df.style.format({
+                'Salary': '{:.2f}',
+                'Proj': '{:.2f}',
+                'Own': '{:.2f}'
+            }).background_gradient(cmap='RdYlGn', axis=0, subset=['Salary', 'Proj', 'Own']), use_container_width=True)
+
+    with st.container():
+        tab1, tab2 = st.tabs(["Display Frequency", "Seed Frame Frequency"])
+        with tab1:
+            if 'data_export_display' in st.session_state:
                 if site_var1 == 'Draftkings':
-                    st.session_state.working_seed = dk_lineups.copy()
+                    player_columns = st.session_state.data_export_display.iloc[:, :8]
                 elif site_var1 == 'Fanduel':
-                    st.session_state.working_seed = fd_lineups.copy()
-            if 'data_export_display' in st.session_state:
-                st.dataframe(st.session_state.data_export_display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), height=500, use_container_width = True)
-            st.download_button(
-                label="Export display optimals",
-                data=convert_df(export_file),
-                file_name='NBA_display_optimals.csv',
-                mime='text/csv',
-            )
-        
-        with st.container():
+                    player_columns = st.session_state.data_export_display.iloc[:, :9]
+                
+                # Flatten the DataFrame and count unique values
+                value_counts = player_columns.values.flatten().tolist()
+                value_counts = pd.Series(value_counts).value_counts()
+                
+                percentages = (value_counts / lineup_num_var * 100).round(2)
+                
+                # Create a DataFrame with the results
+                summary_df = pd.DataFrame({
+                    'Player': value_counts.index,
+                    'Salary': [salary_dict.get(player, player) for player in value_counts.index],
+                    'Frequency': value_counts.values,
+                    'Percentage': percentages.values                        
+                })
+                
+                # Sort by frequency in descending order
+                summary_df = summary_df.sort_values('Frequency', ascending=False)
+                
+                # Display the table
+                st.write("Player Frequency Table:")
+                st.dataframe(summary_df.style.format({'Percentage': '{:.2f}%'}, precision=2), height=500, use_container_width=True)
+            
+                st.download_button(
+                    label="Export player frequency",
+                    data=convert_df_to_csv(summary_df),
+                    file_name='NBA_player_frequency.csv',
+                    mime='text/csv',
+                )
+        with tab2:
             if 'working_seed' in st.session_state:
-                # Create a new dataframe with summary statistics
                 if site_var1 == 'Draftkings':
-                    summary_df = pd.DataFrame({
-                        'Metric': ['Min', 'Average', 'Max', 'STDdev'],
-                        'Salary': [
-                            np.min(st.session_state.working_seed[:,8]),
-                            np.mean(st.session_state.working_seed[:,8]),
-                            np.max(st.session_state.working_seed[:,8]),
-                            np.std(st.session_state.working_seed[:,8])
-                        ],
-                        'Proj': [
-                            np.min(st.session_state.working_seed[:,9]),
-                            np.mean(st.session_state.working_seed[:,9]),
-                            np.max(st.session_state.working_seed[:,9]),
-                            np.std(st.session_state.working_seed[:,9])
-                        ],
-                        'Own': [
-                            np.min(st.session_state.working_seed[:,14]),
-                            np.mean(st.session_state.working_seed[:,14]),
-                            np.max(st.session_state.working_seed[:,14]),
-                            np.std(st.session_state.working_seed[:,14])
-                        ]
-                    })
+                    player_columns = st.session_state.working_seed[:, :8]
                 elif site_var1 == 'Fanduel':
-                    summary_df = pd.DataFrame({
-                        'Metric': ['Min', 'Average', 'Max', 'STDdev'],
-                        'Salary': [
-                            np.min(st.session_state.working_seed[:,9]),
-                            np.mean(st.session_state.working_seed[:,9]),
-                            np.max(st.session_state.working_seed[:,9]),
-                            np.std(st.session_state.working_seed[:,9])
-                        ],
-                        'Proj': [
-                            np.min(st.session_state.working_seed[:,10]),
-                            np.mean(st.session_state.working_seed[:,10]),
-                            np.max(st.session_state.working_seed[:,10]),
-                            np.std(st.session_state.working_seed[:,10])
-                        ],
-                        'Own': [
-                            np.min(st.session_state.working_seed[:,15]),
-                            np.mean(st.session_state.working_seed[:,15]),
-                            np.max(st.session_state.working_seed[:,15]),
-                            np.std(st.session_state.working_seed[:,15])
-                        ]
-                    })
-
-                # Set the index of the summary dataframe as the "Metric" column
-                summary_df = summary_df.set_index('Metric')
-
-                # Display the summary dataframe
-                st.subheader("Optimal Statistics")
-                st.dataframe(summary_df.style.format({
-                    'Salary': '{:.2f}',
-                    'Proj': '{:.2f}',
-                    'Own': '{:.2f}'
-                }).background_gradient(cmap='RdYlGn', axis=0, subset=['Salary', 'Proj', 'Own']), use_container_width=True)
-
-        with st.container():
-            tab1, tab2 = st.tabs(["Display Frequency", "Seed Frame Frequency"])
-            with tab1:
-                if 'data_export_display' in st.session_state:
-                    if site_var1 == 'Draftkings':
-                        player_columns = st.session_state.data_export_display.iloc[:, :8]
-                    elif site_var1 == 'Fanduel':
-                        player_columns = st.session_state.data_export_display.iloc[:, :9]
-                    
-                    # Flatten the DataFrame and count unique values
-                    value_counts = player_columns.values.flatten().tolist()
-                    value_counts = pd.Series(value_counts).value_counts()
-                    
-                    percentages = (value_counts / lineup_num_var * 100).round(2)
-                    
-                    # Create a DataFrame with the results
-                    summary_df = pd.DataFrame({
-                        'Player': value_counts.index,
-                        'Salary': [salary_dict.get(player, player) for player in value_counts.index],
-                        'Frequency': value_counts.values,
-                        'Percentage': percentages.values                        
-                    })
-                    
-                    # Sort by frequency in descending order
-                    summary_df = summary_df.sort_values('Frequency', ascending=False)
-                    
-                    # Display the table
-                    st.write("Player Frequency Table:")
-                    st.dataframe(summary_df.style.format({'Percentage': '{:.2f}%'}, precision=2), height=500, use_container_width=True)
+                    player_columns = st.session_state.working_seed[:, :9]
                 
-                    st.download_button(
-                        label="Export player frequency",
-                        data=convert_df_to_csv(summary_df),
-                        file_name='NBA_player_frequency.csv',
-                        mime='text/csv',
-                    )
-            with tab2:
-                if 'working_seed' in st.session_state:
-                    if site_var1 == 'Draftkings':
-                        player_columns = st.session_state.working_seed[:, :8]
-                    elif site_var1 == 'Fanduel':
-                        player_columns = st.session_state.working_seed[:, :9]
-                    
-                    # Flatten the DataFrame and count unique values
-                    value_counts = player_columns.flatten().tolist()
-                    value_counts = pd.Series(value_counts).value_counts()
-                    
-                    percentages = (value_counts / len(st.session_state.working_seed) * 100).round(2)
-                    # Create a DataFrame with the results
-                    summary_df = pd.DataFrame({
-                        'Player': value_counts.index,
-                        'Salary': [salary_dict.get(player, player) for player in value_counts.index],
-                        'Frequency': value_counts.values,
-                        'Percentage': percentages.values                        
-                    })
-                    
-                    # Sort by frequency in descending order
-                    summary_df = summary_df.sort_values('Frequency', ascending=False)
-                    
-                    # Display the table
-                    st.write("Seed Frame Frequency Table:")
-                    st.dataframe(summary_df.style.format({'Percentage': '{:.2f}%'}, precision=2), height=500, use_container_width=True)
+                # Flatten the DataFrame and count unique values
+                value_counts = player_columns.flatten().tolist()
+                value_counts = pd.Series(value_counts).value_counts()
                 
-                    st.download_button(
-                        label="Export seed frame frequency",
-                        data=convert_df_to_csv(summary_df),
-                        file_name='NBA_seed_frame_frequency.csv',
-                        mime='text/csv',
-                    )
+                percentages = (value_counts / len(st.session_state.working_seed) * 100).round(2)
+                # Create a DataFrame with the results
+                summary_df = pd.DataFrame({
+                    'Player': value_counts.index,
+                    'Salary': [salary_dict.get(player, player) for player in value_counts.index],
+                    'Frequency': value_counts.values,
+                    'Percentage': percentages.values                        
+                })
+                
+                # Sort by frequency in descending order
+                summary_df = summary_df.sort_values('Frequency', ascending=False)
+                
+                # Display the table
+                st.write("Seed Frame Frequency Table:")
+                st.dataframe(summary_df.style.format({'Percentage': '{:.2f}%'}, precision=2), height=500, use_container_width=True)
+            
+                st.download_button(
+                    label="Export seed frame frequency",
+                    data=convert_df_to_csv(summary_df),
+                    file_name='NBA_seed_frame_frequency.csv',
+                    mime='text/csv',
+                )